TY - JOUR
T1 - Transfer learning on convolutional activation feature as applied to a building quality assessment robot
AU - Liu, Lili
AU - Yan, Rui-Jun
AU - Maruvanchery, Varun
AU - Kayacan, Erdal
AU - Chen, I-Meng
AU - Kong Tiong, Lee
PY - 2017/6/8
Y1 - 2017/6/8
N2 - We propose an automated postconstruction quality assessment robot system for crack, hollowness, and finishing defects in light of a need to speed up the inspection work, a more reliable inspection report, as well as an objective through fully automated inspection. Such an autonomous inspection system has a potential to cut labour cost significantly and achieve better accuracy. In the proposed system, a transfer learning network is employed for visual defect detection; a region proposal network is used for object region proposal, a deep learning network employed as feature extractor, and a linear classifier with supervised learning as object classifier; moreover, active learning of top-N ranking region of interest is undertaken for fine-tuning of the transfer learning on convolutional activation feature network. Extensive experiments are validated in a construction quality assessment system room and constructed test bed. The results are promising in a way that the novel proposed automated assessment method gives satisfactory results for crack, hollowness, and finishing defects assessment. To the best of our knowledge, this study is the first attempt to having an autonomous visual inspection system for postconstruction quality assessment of building sector. We believe the proposed system is going to help to pave the way towards fully autonomous postconstruction quality assessment systems in the future.
AB - We propose an automated postconstruction quality assessment robot system for crack, hollowness, and finishing defects in light of a need to speed up the inspection work, a more reliable inspection report, as well as an objective through fully automated inspection. Such an autonomous inspection system has a potential to cut labour cost significantly and achieve better accuracy. In the proposed system, a transfer learning network is employed for visual defect detection; a region proposal network is used for object region proposal, a deep learning network employed as feature extractor, and a linear classifier with supervised learning as object classifier; moreover, active learning of top-N ranking region of interest is undertaken for fine-tuning of the transfer learning on convolutional activation feature network. Extensive experiments are validated in a construction quality assessment system room and constructed test bed. The results are promising in a way that the novel proposed automated assessment method gives satisfactory results for crack, hollowness, and finishing defects assessment. To the best of our knowledge, this study is the first attempt to having an autonomous visual inspection system for postconstruction quality assessment of building sector. We believe the proposed system is going to help to pave the way towards fully autonomous postconstruction quality assessment systems in the future.
KW - Active transfer learning
KW - building quality assessment
KW - deep learning
KW - faster R-CNN
KW - mobile robot
UR - http://www.scopus.com/inward/record.url?scp=85021943207&partnerID=8YFLogxK
U2 - 10.1177/1729881417712620
DO - 10.1177/1729881417712620
M3 - Journal article
SN - 1729-8806
VL - 14
SP - 1
EP - 12
JO - International Journal of Advanced Robotic Systems
JF - International Journal of Advanced Robotic Systems
IS - 3
ER -